Road geometry modelling is very useful for map creation and terrain identification along with feature and obstacle detection in environments, each of which may facilitate autonomous vehicle navigation along a prescribed path. Traditional methods for modelling of road geometry and object or feature detection are resource intensive, often requiring significant amounts of human measurement and calculation. Such methods are thus time consuming and costly. Exacerbating this issue is the fact that many modern day applications require the analysis of large amounts of data, and therefore are not practical without quicker or less costly techniques.
Some current methods rely upon feature detection from image data to model or reproduce signs or other points of interest along a route. However, while images of an environment may remain static and unchanging, the environment itself may change routinely. For example, signs that provide an indication of a road condition may change season-to-season, while signs that provide an indication of a business may change based upon a change in the business at a particular location. Further, signs that indicate a price for services or products, such as the price of gasoline or the price of a hotel room, may change daily, weekly, or according to almost any frequency. It is not feasible to obtain images of an environment each time a sign changes or another feature of the environment changes.